• Journal of Technologic Dentistry
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• v.33 no.4
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• pp.427-440
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• 2011

Purpose: The following research analyzes the causes of major selection and career decision of students studying dental technology. It is to be used as basic data for the management of career improvement program. Methods: The survey has been processed to 490 college students studying Dental Technology in D-college. Questionnaire consists of major selection confidence sheet (14 items) and career decision confidence sheet (18 items) and was scored with 5-points per question. The collected data was analyzed by the statistical program: SAS V8 for Windows. To test for significance on each item, p < 0.05 has been decided as a standard. Results: The analysis of result about the level of confidence on major selection has valid difference by genders, serving military service or not, experience of studying one more year to enter the college or not, making career decision and grade. The analysis of result about career decision has valid difference by gender, serving military service, career decision, day and night course, age and native place. Conclusion: We develop the career advice program and manage it effectively, the confidence on the major selection and pride about its faculty will be high to dental technology students.

• International Journal of Costume and Fashion
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• v.12 no.1
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• pp.29-48
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• 2012

The purpose of this study was to examine the relationship between consumer spatial behavior and consumer characteristics based on trade area selection criteria 469 female consumers who lived in the two new towns near Seoul, Bundang and Ilsan, participated in the study by completing questionnaires. Data were analyzed by using cluster analysis, ANOVA, Duncan's multiple range test, chi-square analysis, etc. The findings of the empirical research were as follows: 1. Five groups were identified by cluster analysis based on trade area selection criteria of clothing price-oriented group, time convenience-oriented group, shopping convenience-oriented group, variety/entertainment-oriented group, and passive shopping group. 2. Each group differed in spatial behavior such as clothing shopping area, the visiting frequency, and spatial movement type. 3. Each group showed differences in fashion involvement and demographic characteristics(age, marital status, education, occupation and social status).

• International Journal of Costume and Fashion
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• v.10 no.2
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• pp.51-59
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• 2010

This study regarded fashion selection criteria as clothing consumption value and desired fashion images, and examined selection differences according to regional subculture groups. Clothing consumption value is a direct value that people seek with clothing products and a perceived value which is divided into emotional, social, price, quality values. Fashion image which is a feeling communicated to others by wearing a certain fashion style is the most superficial value. Multivariate Analysis of Variance (MANOVA) was performed to test the differences between regional subculture groups in clothing consumption values and desired fashion images. We found some differences in clothing consumption value specifically in emotional value and social value. The group differences were remarkably significant in fashion image comparison. 'Kang-nam' group pursued 'lively', 'sophisticated', 'charming', feminine', 'gorgeous' image more than 'Kang-buk' group. While 'Kang-buk' group produced lower scores in ideal fashion images, the group had significant higher seeking in 'sportive' image compared to 'Kangnam' group.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.323-325
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• 2021

In this article, we have improved the prediction of hypertension detection using the feature selection method for the Korean national health data named by the KNHANES database. The study identified a variety of risk factors associated with chronic hypertension. The paper is divided into two modules. The first of these is a data pre-processing step that uses a factor analysis (FA) based feature selection method from the dataset. The next module applies a predictive analysis step to detect and predict hypertension risk prediction. In this study, we compare the mean standard error (MSE), F1-score, and area under the ROC curve (AUC) for each classification model. The test results show that the proposed FIFA-OE-NB algorithm has an MSE, F1-score, and AUC outcomes 0.259, 0.460, and 64.70%, respectively. These results demonstrate that the proposed FIFA-OE method outperforms other models for hypertension risk predictions.

• Advances in environmental research
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• v.7 no.1
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• pp.1-11
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• 2018

Textile factories are one of the industries which its wastewater treatment is a challenging issue, especially in developing countries and a conventional treatment cannot treat all its pollutants properly. Using chemical coagulants is a technique for physical and chemical primary treatment of the wastewater. We applied jar test for selection of suitable coagulant among the five coagulants including alum, calcium hydroxide, ferrous sulfate, ferrous chloride and barium chloride for the effluent of wastewater in Mazandran textile factory located in Mazandran Province, Iran. In addition, jar test, we also used analytic hierarchy process (AHP) method considering criteria which included coagulation cost, sensitivity to pH change, the amount of sludge generation and side effects for coagulation. The results of the jar test indicated that calcium hydroxide was proper among the coagulants which it removed 92.9% total suspended solid (TSS), 70% dye and 30% chemical oxygen demand. The AHP analysis presented that calcium hydroxide is more suitable than other coagulants considering five criteria.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.21-33
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• 2014

PURPOSES: The purpose of this study was to break away from the workforce method using cold-mix asphalt mixtures and has a constant quality and has develop repair materials of pre-production asphalt-precast types. METHODS: The selection of the repair material was determined as the results obtained through physical properties of materials and the field applicability. In case of repair materials, values obtained through Marshall stability test & the dynamic stability test & retained stability test as well as the site conditions was considered. In case of adhesive, test results were obtained through examination of the bond strength(tensile, shear) and the field applicability of the adhesive was examined through combined specimens to simulate field applications. RESULTS : According to the results of laboratory tests, in the case of repair materials, Marshall stability and dynamic stability, retained stability of cold-mix reaction type asphalt mixture is the highest. In the case of adhesive, two-component epoxy-urea has a very high bonding strength(tensile, shear) was most excellent. According to the results of field tests, when epoxy-urea was excellent workability. Also, the repair body through actual mock-up test did not occur large deformation and fracture after 12 months. CONCLUSIONS : A suitable repair material is cold-mix reaction type mixture of asphalt-precast, a suitable adhesive is a two-component epoxy-urea.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.43-50
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• 2010

In this study, we propose a new order selection method for clinical pathway development in acute appendectomy. This method is based on the lift concept which is popular in association rule discovery and, starting from the orders with more frequencies, sequentially removes the negatively associated orders which have lift values somewhat less than one. The orders in acute appendectomy we consider in this study are test and medical treatment items respectively, and since there are different order patterns before, during, and after operation, three different order selections are made for each. The selection results are somewhat different from those selected only by the order of more frequencies. Specifically, the selection results of two methods are different in 1 or 2 orders for medical treatment items and in maximum 5 orders for test items, respectively.

• Journal of Korean Academy of Nursing
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• v.37 no.3
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• pp.414-421
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• 2007

Background: Nursing intervention studies often suffer from a selection bias introduced by failure of random assignment. Evaluation with selection bias could under or over-estimate any intervention's effects. PS matching (PSM) can reduce a selection bias through matching similar Propensity Scores (PS). PS is defined as the conditional probability of being treated given the individual's covariates and it can be reused to balance the covariates of two groups. Purpose: This study was done to assess the significance of PSM as an alternative evaluation method of nursing interventions. Method: An intervention study for patients with some baseline individual characteristic differences between two groups was used for this demonstration. The result of a t-test with PSM was compared with a t-test without matching. Results: The level of HbA1c at 12 months after baseline was different between the two groups in terms of matching or not. Conclusion: This study demonstrated the effects of a quasi-random assignment. Evaluation using PSM can reduce a selection bias impact that affects the result of the nursing intervention. Analyzing nursing research more objectively to reduce selection bias using PSM is needed.

• The Bulletin of The Korean Astronomical Society
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• v.30 no.2
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• pp.82.2-82.2
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• 2005

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1684-1690
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• 2004

This paper presents a method for classifier selection that uses distribution information of the training samples in a small region surrounding a sample. The conventional DCS-LA(Dynamic Classifier Selection - Local Accuracy) selects a classifier dynamically by comparing the local accuracy of each classifier at the test time, which inevitably requires long classification time. On the other hand, in the proposed approach, the best classifier in a local region is stored in the FSA(Feature Space Attribute) table during the training time, and the test is done by just referring to the table. Therefore, this approach enables fast classification because classification is not needed during test. Two feature space attributes are used entropy and density of k training samples around each sample. Each sample in the feature space is mapped into a point in the attribute space made by two attributes. The attribute space is divided into regular rectangular cells in which the local accuracy of each classifier is appended. The cells with associated local accuracy comprise the FSA table. During test, when a test sample is applied, the cell to which the test sample belongs is determined first by calculating the two attributes, and then, the most accurate classifier is chosen from the FSA table. To show the effectiveness of the proposed algorithm, it is compared with the conventional DCS -LA using the Elena database. The experiments show that the accuracy of the proposed algorithm is almost same as DCS-LA, but the classification time is about four times faster than that.